Overview

Dataset statistics

Number of variables9
Number of observations13129
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory923.3 KiB
Average record size in memory72.0 B

Variable types

Numeric9

Warnings

track_id has unique values Unique
danceability has unique values Unique
energy has unique values Unique
speechiness has unique values Unique

Reproduction

Analysis started2021-02-20 09:41:57.671348
Analysis finished2021-02-20 09:42:06.524729
Duration8.85 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

track_id
Real number (ℝ≥0)

UNIQUE

Distinct13129
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34031.05827
Minimum2
Maximum124911
Zeros0
Zeros (%)0.0%
Memory size102.7 KiB
2021-02-20T10:42:06.581697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1609.4
Q112986
median28097
Q345021
95-th percentile105011.6
Maximum124911
Range124909
Interquartile range (IQR)32035

Descriptive statistics

Standard deviation28950.42218
Coefficient of variation (CV)0.8507059038
Kurtosis1.453502919
Mean34031.05827
Median Absolute Deviation (MAD)15740
Skewness1.345293707
Sum446793764
Variance838126944.5
MonotocityStrictly increasing
2021-02-20T10:42:06.670796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81881
 
< 0.1%
397661
 
< 0.1%
418411
 
< 0.1%
234061
 
< 0.1%
643621
 
< 0.1%
930321
 
< 0.1%
70141
 
< 0.1%
336371
 
< 0.1%
152021
 
< 0.1%
111041
 
< 0.1%
Other values (13119)13119
99.9%
ValueCountFrequency (%)
21
< 0.1%
31
< 0.1%
51
< 0.1%
101
< 0.1%
1341
< 0.1%
ValueCountFrequency (%)
1249111
< 0.1%
1248641
< 0.1%
1248631
< 0.1%
1248621
< 0.1%
1248571
< 0.1%

acousticness
Real number (ℝ≥0)

Distinct12905
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5246876457
Minimum9.035 × 107
Maximum0.9957964501
Zeros0
Zeros (%)0.0%
Memory size102.7 KiB
2021-02-20T10:42:06.749693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9.035 × 107
5-th percentile0.00084733888
Q10.1037725546
median0.5739848424
Q30.9207269763
95-th percentile0.9949785144
Maximum0.9957964501
Range0.9957955466
Interquartile range (IQR)0.8169544217

Descriptive statistics

Standard deviation0.3837185744
Coefficient of variation (CV)0.7313276337
Kurtosis-1.61854111
Mean0.5246876457
Median Absolute Deviation (MAD)0.3836039422
Skewness-0.1335961627
Sum6888.624101
Variance0.1472399443
MonotocityNot monotonic
2021-02-20T10:42:06.828719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9957964501217
 
1.7%
0.99576143062
 
< 0.1%
0.99556039232
 
< 0.1%
0.99546703392
 
< 0.1%
0.99576993412
 
< 0.1%
0.9956457992
 
< 0.1%
0.99568395842
 
< 0.1%
0.99567127572
 
< 0.1%
0.99563483472
 
< 0.1%
0.84503873641
 
< 0.1%
Other values (12895)12895
98.2%
ValueCountFrequency (%)
9.035 × 1071
< 0.1%
9.491 × 1071
< 0.1%
9.575 × 1071
< 0.1%
1.0362 × 1061
< 0.1%
1.1568 × 1061
< 0.1%
ValueCountFrequency (%)
0.9957964501217
1.7%
0.99579618741
 
< 0.1%
0.99579604941
 
< 0.1%
0.99578713461
 
< 0.1%
0.99578556991
 
< 0.1%

danceability
Real number (ℝ≥0)

UNIQUE

Distinct13129
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4872904971
Minimum0.051307487
Maximum0.9686446617
Zeros0
Zeros (%)0.0%
Memory size102.7 KiB
2021-02-20T10:42:06.908013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.051307487
5-th percentile0.1741972247
Q10.3447591127
median0.485634814
Q30.6290940727
95-th percentile0.7994442269
Maximum0.9686446617
Range0.9173371747
Interquartile range (IQR)0.28433496

Descriptive statistics

Standard deviation0.190148007
Coefficient of variation (CV)0.3902148885
Kurtosis-0.73490098
Mean0.4872904971
Median Absolute Deviation (MAD)0.1422164698
Skewness0.02854011807
Sum6397.636937
Variance0.03615626456
MonotocityNot monotonic
2021-02-20T10:42:06.981776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.57084222571
 
< 0.1%
0.66003117041
 
< 0.1%
0.35141781921
 
< 0.1%
0.69807592851
 
< 0.1%
0.8252581791
 
< 0.1%
0.62456124671
 
< 0.1%
0.65558311121
 
< 0.1%
0.65131661561
 
< 0.1%
0.40333531641
 
< 0.1%
0.72164676331
 
< 0.1%
Other values (13119)13119
99.9%
ValueCountFrequency (%)
0.0513074871
< 0.1%
0.05143514191
< 0.1%
0.0516357471
< 0.1%
0.05166771271
< 0.1%
0.05283487971
< 0.1%
ValueCountFrequency (%)
0.96864466171
< 0.1%
0.96688316441
< 0.1%
0.96187069421
< 0.1%
0.9615473591
< 0.1%
0.95523948371
< 0.1%

energy
Real number (ℝ≥0)

UNIQUE

Distinct13129
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5375155316
Minimum2.01659 × 105
Maximum0.9999637108
Zeros0
Zeros (%)0.0%
Memory size102.7 KiB
2021-02-20T10:42:07.058420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.01659 × 105
5-th percentile0.05641813316
Q10.3213003717
median0.5491128455
Q30.7762541942
95-th percentile0.9486446235
Maximum0.9999637108
Range0.9999435449
Interquartile range (IQR)0.4549538225

Descriptive statistics

Standard deviation0.2780488872
Coefficient of variation (CV)0.5172853078
Kurtosis-1.031884058
Mean0.5375155316
Median Absolute Deviation (MAD)0.2276580579
Skewness-0.1925456384
Sum7057.041414
Variance0.07731118367
MonotocityNot monotonic
2021-02-20T10:42:07.134510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.48048352051
 
< 0.1%
0.92432035961
 
< 0.1%
0.49646550611
 
< 0.1%
0.45304680491
 
< 0.1%
0.55917305141
 
< 0.1%
0.56983817941
 
< 0.1%
0.39807201631
 
< 0.1%
0.5584577291
 
< 0.1%
0.92859700861
 
< 0.1%
0.13894755861
 
< 0.1%
Other values (13119)13119
99.9%
ValueCountFrequency (%)
2.01659 × 1051
< 0.1%
2.02817 × 1051
< 0.1%
2.03071 × 1051
< 0.1%
2.03104 × 1051
< 0.1%
2.03357 × 1051
< 0.1%
ValueCountFrequency (%)
0.99996371081
< 0.1%
0.99995040591
< 0.1%
0.99988214621
< 0.1%
0.9997675991
< 0.1%
0.99973180431
< 0.1%

instrumentalness
Real number (ℝ≥0)

Distinct12969
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6405359213
Minimum0
Maximum0.9980162209
Zeros38
Zeros (%)0.3%
Memory size102.7 KiB
2021-02-20T10:42:07.215232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.417644 × 105
Q10.323466117
median0.8381337604
Q30.9182439469
95-th percentile0.9637844918
Maximum0.9980162209
Range0.9980162209
Interquartile range (IQR)0.5947778299

Descriptive statistics

Standard deviation0.3614301125
Coefficient of variation (CV)0.5642620508
Kurtosis-0.9161849462
Mean0.6405359213
Median Absolute Deviation (MAD)0.1087424846
Skewness-0.8837819104
Sum8409.596111
Variance0.1306317263
MonotocityNot monotonic
2021-02-20T10:42:07.292674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.972870004351
 
0.4%
038
 
0.3%
3 × 101012
 
0.1%
2 × 10109
 
0.1%
1 × 10109
 
0.1%
1 × 1097
 
0.1%
6 × 10105
 
< 0.1%
5 × 10104
 
< 0.1%
2.1 × 1094
 
< 0.1%
4 × 10104
 
< 0.1%
Other values (12959)12986
98.9%
ValueCountFrequency (%)
038
0.3%
1 × 10109
 
0.1%
2 × 10109
 
0.1%
3 × 101012
 
0.1%
4 × 10104
 
< 0.1%
ValueCountFrequency (%)
0.99801622091
< 0.1%
0.99712094451
< 0.1%
0.99313427951
< 0.1%
0.99102244741
< 0.1%
0.99101884571
< 0.1%

liveness
Real number (ℝ≥0)

Distinct13128
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1878036461
Minimum0.0252973415
Maximum0.9803300006
Zeros0
Zeros (%)0.0%
Memory size102.7 KiB
2021-02-20T10:42:07.372902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0252973415
5-th percentile0.07088730634
Q10.1014056421
median0.1190017893
Q30.2110407548
95-th percentile0.5661655007
Maximum0.9803300006
Range0.9550326591
Interquartile range (IQR)0.1096351127

Descriptive statistics

Standard deviation0.1580505933
Coefficient of variation (CV)0.8415736148
Kurtosis5.686617057
Mean0.1878036461
Median Absolute Deviation (MAD)0.0301656935
Skewness2.340950399
Sum2465.674069
Variance0.02497999005
MonotocityNot monotonic
2021-02-20T10:42:07.449621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11156801632
 
< 0.1%
0.11167394131
 
< 0.1%
0.60844645321
 
< 0.1%
0.38485406921
 
< 0.1%
0.33778813371
 
< 0.1%
0.14588849721
 
< 0.1%
0.28969607341
 
< 0.1%
0.07928471791
 
< 0.1%
0.11149399441
 
< 0.1%
0.08584494241
 
< 0.1%
Other values (13118)13118
99.9%
ValueCountFrequency (%)
0.02529734151
< 0.1%
0.02591638241
< 0.1%
0.02629099031
< 0.1%
0.02658936571
< 0.1%
0.02704013331
< 0.1%
ValueCountFrequency (%)
0.98033000061
< 0.1%
0.97196847231
< 0.1%
0.97139229391
< 0.1%
0.97092393081
< 0.1%
0.97003529531
< 0.1%

speechiness
Real number (ℝ≥0)

UNIQUE

Distinct13129
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09917423838
Minimum0.0223236675
Maximum0.9661774074
Zeros0
Zeros (%)0.0%
Memory size102.7 KiB
2021-02-20T10:42:07.529107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0223236675
5-th percentile0.02906533834
Q10.0369324895
median0.049019219
Q30.0854524094
95-th percentile0.3997288035
Maximum0.9661774074
Range0.9438537399
Interquartile range (IQR)0.0485199199

Descriptive statistics

Standard deviation0.1373805072
Coefficient of variation (CV)1.385243884
Kurtosis14.21318349
Mean0.09917423838
Median Absolute Deviation (MAD)0.0157842236
Skewness3.535996513
Sum1302.058576
Variance0.01887340375
MonotocityNot monotonic
2021-02-20T10:42:07.603217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03962973761
 
< 0.1%
0.06596409851
 
< 0.1%
0.05056439261
 
< 0.1%
0.03721655931
 
< 0.1%
0.05402187171
 
< 0.1%
0.10727443131
 
< 0.1%
0.05617346591
 
< 0.1%
0.56639858891
 
< 0.1%
0.03763777521
 
< 0.1%
0.03962825711
 
< 0.1%
Other values (13119)13119
99.9%
ValueCountFrequency (%)
0.02232366751
< 0.1%
0.02266344261
< 0.1%
0.02279467311
< 0.1%
0.02284920471
< 0.1%
0.02300598631
< 0.1%
ValueCountFrequency (%)
0.96617740741
< 0.1%
0.96437689371
< 0.1%
0.96407346031
< 0.1%
0.96305269251
< 0.1%
0.96046098031
< 0.1%

tempo
Real number (ℝ≥0)

Distinct11902
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.080061
Minimum12.753
Maximum251.072
Zeros0
Zeros (%)0.0%
Memory size102.7 KiB
2021-02-20T10:42:07.676385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum12.753
5-th percentile72.452
Q195.967
median120.057
Q3145.318
95-th percentile185.7352
Maximum251.072
Range238.319
Interquartile range (IQR)49.351

Descriptive statistics

Standard deviation35.01513746
Coefficient of variation (CV)0.2844907386
Kurtosis0.04913710758
Mean123.080061
Median Absolute Deviation (MAD)24.723
Skewness0.4515204074
Sum1615918.121
Variance1226.059851
MonotocityNot monotonic
2021-02-20T10:42:07.748896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120.0077
 
0.1%
120.0047
 
0.1%
100.016
 
< 0.1%
120.016
 
< 0.1%
120.0286
 
< 0.1%
120.0216
 
< 0.1%
129.995
 
< 0.1%
100.0095
 
< 0.1%
120.0065
 
< 0.1%
139.9925
 
< 0.1%
Other values (11892)13071
99.6%
ValueCountFrequency (%)
12.7531
< 0.1%
19.6591
< 0.1%
26.1311
< 0.1%
27.4931
< 0.1%
29.0931
< 0.1%
ValueCountFrequency (%)
251.0721
< 0.1%
250.0591
< 0.1%
249.6161
< 0.1%
249.3651
< 0.1%
247.7911
< 0.1%

valence
Real number (ℝ≥0)

Distinct13127
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4397607641
Minimum1 × 105
Maximum0.99999
Zeros0
Zeros (%)0.0%
Memory size102.7 KiB
2021-02-20T10:42:07.824560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 105
5-th percentile0.03947985412
Q10.1973211315
median0.417742892
Q30.6655754057
95-th percentile0.9168766766
Maximum0.99999
Range0.99998
Interquartile range (IQR)0.4682542742

Descriptive statistics

Standard deviation0.2760283032
Coefficient of variation (CV)0.6276783326
Kurtosis-1.092554418
Mean0.4397607641
Median Absolute Deviation (MAD)0.2328436598
Skewness0.2329070118
Sum5773.619072
Variance0.07619162414
MonotocityNot monotonic
2021-02-20T10:42:07.901885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.999993
 
< 0.1%
0.62068296231
 
< 0.1%
0.08429285141
 
< 0.1%
0.86530799111
 
< 0.1%
0.92365970861
 
< 0.1%
0.03972390561
 
< 0.1%
0.96406584391
 
< 0.1%
0.50301232991
 
< 0.1%
0.79951132921
 
< 0.1%
0.0418698721
 
< 0.1%
Other values (13117)13117
99.9%
ValueCountFrequency (%)
1 × 1051
< 0.1%
0.00224537151
< 0.1%
0.00869494091
< 0.1%
0.00905884961
< 0.1%
0.0105276621
< 0.1%
ValueCountFrequency (%)
0.999993
< 0.1%
0.99258272781
 
< 0.1%
0.99113221051
 
< 0.1%
0.98943831761
 
< 0.1%
0.98726694631
 
< 0.1%

Interactions

2021-02-20T10:42:00.054237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:00.157965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:00.229018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:00.295985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:00.364087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:00.432424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:00.502669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:00.570295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:00.636909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:00.704406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:00.770107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:00.835125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:00.901526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:00.967413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:01.031922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:01.095461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:01.161898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:01.226909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:01.292286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:01.355132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:01.419553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:01.481039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:01.544257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:01.605406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:01.667592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:01.735130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:01.803136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:01.867151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:01.938822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:02.004410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:02.068370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:02.131553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:02.195982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:02.264943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:02.330769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:02.394389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:02.460027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:02.523978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:02.590117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:02.652514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:02.718733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:02.786912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:02.852771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:02.916391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:02.981201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:03.052022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:03.116359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:03.179446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:03.243150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:03.310420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:03.376650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:03.440423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:03.505545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:03.571384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:03.636050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:03.700457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:03.766792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:03.831962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:03.893294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:03.961492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:04.023615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:04.087967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:04.152171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:04.215565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:04.276539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:04.343901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:04.410525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:04.478366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:04.544255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:04.608493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:04.674004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-20T10:42:04.739979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-02-20T10:42:07.968174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-20T10:42:08.062872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-20T10:42:08.153698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-20T10:42:08.245599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-02-20T10:42:06.322331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-20T10:42:06.452884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

track_idacousticnessdanceabilityenergyinstrumentalnesslivenessspeechinesstempovalence
020.4166750.6758940.6344760.0106280.1776470.159310165.9220.576661
130.3744080.5286430.8174610.0018510.1058800.461818126.9570.269240
250.0435670.7455660.7014700.0006970.3731430.124595100.2600.621661
3100.9516700.6581790.9245250.9654270.1154740.032985111.5620.963590
41340.4522170.5132380.5604100.0194430.0965670.525519114.2900.894072
51390.1065500.2609110.6070670.8350870.2236760.030569196.9610.160267
61400.3763120.7340790.2656850.6695810.0859950.039068107.9520.609991
71410.9636570.4359330.0756320.3454930.1056860.02665833.4770.163950
81420.6628810.3790650.8238560.9102660.0887050.079090147.7810.092868
91440.9090110.4436430.6419970.9240920.2676690.089659128.5370.788251

Last rows

track_idacousticnessdanceabilityenergyinstrumentalnesslivenessspeechinesstempovalence
131191248520.7025530.7289000.5906360.4150600.1184250.037389100.0000.198466
131201248530.0203070.6895750.6763060.8918170.0959150.030968120.0230.438201
131211248540.0265450.6673630.6573990.3641070.1080310.042832121.0040.460545
131221248550.1602810.6042490.5542420.7286960.3512490.175388167.9750.402043
131231248560.1122680.5812550.6659630.8530410.1009660.041647121.0100.387918
131241248570.0075920.7903640.7192880.8531140.7207150.082550141.3320.890461
131251248620.0414980.8430770.5364960.8651510.5479490.074001101.9750.476845
131261248630.0001240.6096860.8951360.8466240.6329030.051517129.9960.496667
131271248640.3275760.5744260.5483270.4528670.0759280.033388142.0090.569274
131281249110.9936060.4993390.0506220.9456770.0959650.065189119.9650.204652